Performance comparison of multifarious deep networks on caries detection with tooth X-ray images.

Objectives: Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the caries detection performances of recent multifarious deep networks with clinical dentist level as a bridge.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review.

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics.

Numerical hemolysis performance evaluation of a rotary blood pump under different speed modulation profiles.

Speed modulation methods have been studied and even used clinically to create extra pulsation in the blood circulatory system with the assistance of a continuous flow rotary blood pump. However, fast speed variations may also increase the hemolysis potential inside the pump. This study investigates the hemolysis performance of a ventricular assist rotary blood pump under sinusoidal, square, and triangular wave speed modulation profiles using the computational fluid dynamics (CFD) method.

Outcome of teeth restored with CAD/CAM zirconium dioxide post-cores: a retrospective study with a follow-up period of 3-6 years.

Statement Of Problem: Computer aided design/computer aided manufacturing (CAD/CAM) zirconia post-cores is one of the options of post crown restoration materials due to their esthetic properties and superior mechanical strength. However, the clinical effect on aesthetics and strength properties is unclear due to the lack of results of their long-term follow-up.

Purpose: This retrospective clinical study aims to analyze the survival rate, clinical manifestations, and failure factors after CAD/CAM zirconia post-core restoration.

Caries segmentation on tooth X-ray images with a deep network.

Objectives: Deep learning has been a promising technology in many biomedical applications. In this study, a deep network was proposed aiming for caries segmentation on the clinically collected tooth X-ray images.

Methods: The proposed network inherited the skip connection characteristic from the widely used U-shaped network, and creatively adopted vision Transformer, dilated convolution, and feature pyramid fusion methods to enhance the multi-scale and global feature extraction capability.

On-line parameter identification of the lumped arterial system model: A simulation study.

A lumped model of the arterial system has been used in constructing a hybrid mock loop due to its real-time response. However, the parameters of the model are always from a general case and not adapted to a specific patient. In this study, we focused on on-line parameter identification of the lumped model of the arterial system that could be used for a specific patient.